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Objectives <p>To determine the accuracy of Face2Gene (F2G) app in the diagnosis of a genetic syndrome as a first correct response, after uploading the image of the patient in the app (top 1 accuracy), first 3 responses (top 3 accuracy), and first 10 responses (top 10 accuracy) out of 30 differential diagnoses given by the app. Also, to determine the accuracy of the app for rare and ultra-rare diagnoses given by the app.</p> Methods <p>Frontal facial images of individuals with the diagnosis of a genetic syndrome (established clinically or molecularly) were analysed with and without additional clinical features.</p> Results <p>In this study, a total of 118 children were recruited. Overall, the molecularly confirmed eventual diagnosis appeared in the “top 10” suggested syndromes by Face2Gene in 75/118 cases, providing a diagnostic yield of 63.6%. In this study, the top 1 accuracy for correct first diagnosis by the app and clinician’s first diagnosis was 45.8% (<i>n</i> = 54). The Mcnemar test was examined for the clinician’s accurate diagnosis as compared to top 1, top 3, and top 10 accuracy by the app and the <i>p</i>-value was statistically significant for top 10 accuracy (0.0005) and not for the top 1 and 3 diagnoses. The top 10 accuracy for the app in the rare cases was 21/30 cases (70%), and for ultra-rare cases was 28/64 (43.8%).</p> Conclusions <p>The Face2Gene app is useful as an assistant to clinicians in the diagnosis of rare and ultra-rare diseases. The top 10 accuracy is better than clinical diagnosis, and the yield is better for the ultra-rare cases and the single gene disease category, too.</p>

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The Utility of Face2Gene App for Syndrome Recognition in Indian Children with Dysmorphism

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Mehak Malhotra,
  • Suvarna Magar,
  • Madhavi Shelke,
  • Varsha Vaidya,
  • Sandip Saraf,
  • Ashka Prajapati,
  • Udhaya Kotecha,
  • Pratibha Pawal,
  • Tushar Idhate,
  • Avinash Sangle,
  • Ghansham Magar,
  • Anjali Kale,
  • Gunjan Gandhi,
  • Madhuri Engade,
  • Saeed Siddique,
  • Sachin Khambayate,
  • Karthik Akunuri

摘要

Objectives

To determine the accuracy of Face2Gene (F2G) app in the diagnosis of a genetic syndrome as a first correct response, after uploading the image of the patient in the app (top 1 accuracy), first 3 responses (top 3 accuracy), and first 10 responses (top 10 accuracy) out of 30 differential diagnoses given by the app. Also, to determine the accuracy of the app for rare and ultra-rare diagnoses given by the app.

Methods

Frontal facial images of individuals with the diagnosis of a genetic syndrome (established clinically or molecularly) were analysed with and without additional clinical features.

Results

In this study, a total of 118 children were recruited. Overall, the molecularly confirmed eventual diagnosis appeared in the “top 10” suggested syndromes by Face2Gene in 75/118 cases, providing a diagnostic yield of 63.6%. In this study, the top 1 accuracy for correct first diagnosis by the app and clinician’s first diagnosis was 45.8% (n = 54). The Mcnemar test was examined for the clinician’s accurate diagnosis as compared to top 1, top 3, and top 10 accuracy by the app and the p-value was statistically significant for top 10 accuracy (0.0005) and not for the top 1 and 3 diagnoses. The top 10 accuracy for the app in the rare cases was 21/30 cases (70%), and for ultra-rare cases was 28/64 (43.8%).

Conclusions

The Face2Gene app is useful as an assistant to clinicians in the diagnosis of rare and ultra-rare diseases. The top 10 accuracy is better than clinical diagnosis, and the yield is better for the ultra-rare cases and the single gene disease category, too.